تقرير
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
| العنوان: | Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech |
|---|---|
| المؤلفون: | Popov, Vadim, Vovk, Ivan, Gogoryan, Vladimir, Sadekova, Tasnima, Kudinov, Mikhail |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computation and Language, Statistics - Machine Learning |
| الوصف: | Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Grad-TTS, a novel text-to-speech model with score-based decoder producing mel-spectrograms by gradually transforming noise predicted by encoder and aligned with text input by means of Monotonic Alignment Search. The framework of stochastic differential equations helps us to generalize conventional diffusion probabilistic models to the case of reconstructing data from noise with different parameters and allows to make this reconstruction flexible by explicitly controlling trade-off between sound quality and inference speed. Subjective human evaluation shows that Grad-TTS is competitive with state-of-the-art text-to-speech approaches in terms of Mean Opinion Score. We will make the code publicly available shortly. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2105.06337 |
| رقم الأكسشن: | edsarx.2105.06337 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |